Real-Time Identification of Cricothyrotomy Landmarks in Emergency Care and Obstetric Patients Using Wireless Handheld Ultrasound and Edge-Computing Artificial Intelligence: A Prospective Observational Study.
This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participants presenting to the emergency department with dys...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
10/10/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188548168&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188548168 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 10/10/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188548168 188548168 188548168 10.1007/s10916-025-02275-z 188548168 ppf: 1 ppct: 12 formats: tig: atl: Real-Time Identification of Cricothyrotomy Landmarks in Emergency Care and Obstetric Patients Using Wireless Handheld Ultrasound and Edge-Computing Artificial Intelligence: A Prospective Observational Study. aug: au: Wu, Cheng-Yi Li, Jia-Da Shih, Po-Yuan Huang, Cheng-Chia Cheng, Hsiao-Liang Wu, Chun-Yu Tay, Joyce Wu, Meng-Che Wang, Chih-Hung Chen, Chu-Song Huang, Chien-Hua affil: https://ror.org/03nteze27 Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan sug: subj: Machine Learning Algorithms Detection Algorithms Cricoid Cartilage Ultrasonography Thyroid Cartilage Ultrasonography Membranes Ultrasonography Cricothyrotomy Methods Ultrasonography Equipment and Supplies Obstetric Patients Emergency Care Human China Funding Source Female Adult Middle Age Aged Prospective Studies Nonexperimental Studies Hospitals, Public Checklists Sensitivity and Specificity Predictive Value of Tests ROC Curve Kruskal-Wallis Test Chi Square Test Friedman Test Data Analysis Software Confidence Intervals Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female ab: This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participants presenting to the emergency department with dyspnea or to the obstetrics and gynecology department for a scheduled cesarean section between August 2022 and July 2024 were prospectively recruited. Ultrasonographic images were collected using a wireless handheld ultrasound device connected to an edge computing tablet. Three You Only Look Once (YOLO) model variants—v5n6, v8n, and v10n—were selected for development and evaluation. A total of 608 participants (median age: 58.0 years, interquartile range [IQR]: 40.0–73.0; median body mass index: 23.2 kg/m², IQR: 20.2–26.5) contributed 117,094 ultrasonographic frames. All three YOLO-based models demonstrated high accuracy in detecting CC, TC, and CTM, with area under the receiver operating characteristic curve values exceeding 0.88. In correctly identified frames, the models effectively localized CC (IOU values: YOLOv5n6, 0.713 [95% confidence interval (CI): 0.698–0.726]; YOLOv8n, 0.718 [95% CI: 0.702–0.733]; YOLOv10n, 0.718 [95% CI: 0.701–0.734]; p value: 0.03) and TC (YOLOv5n6, 0.700 [95% CI: 0.683–0.717]; YOLOv8n, 0.706 [95% CI: 0.687–0.725]; YOLOv10n, 0.703 [95% CI: 0.783–0.721] ; p value: 0.037), though localization accuracy was lower for CTM (YOLOv5n6, 0.364 [95% CI: 0.333–0.394]; YOLOv8n, 0.363 [95% CI: 0.331–0.394]; YOLOv10n, 0.354 [95% CI: 0.325–0.381] ; p value: 0.053). The mean frames per second for YOLOv5n6, YOLOv8n, and YOLOv10n were 3.67, 13.83, and 14.13, respectively, when deployed on the handheld ultrasound platform. YOLO-based models demonstrated high accuracy in detecting and localizing CC, TC, and CTM. YOLOv8n and YOLOv10n achieved clinically acceptable real-time imaging performance when deployed on a wireless handheld ultrasound device with an edge computing tablet. Further studies are needed to assess whether this favorable performance translates into actual clinical benefits. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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